AI 中文总结
本研究在O-RAN ISAC仿真平台上，基于5G上行链路信号，评估多无人机的检测、关联与跟踪性能，明确了影响其跟踪表现的关键因素。
AI 中文摘要
我们在由OpenAirInterface、FlexRIC和Sionna RT构建的O-RAN仿真测试平台上，对多目标检测、关联与跟踪进行端到端评估，该平台将5G NR上行链路探测参考信号重新用作无源雷达波形，评估依据是反无人机系统（counter-UAS）指挥控制（C2）消费者的需求，而非仅依赖检测性能。3架无人机在高度、速度和雷达散射截面（-8至-20 dBsm）上存在差异，形成一个双基地对，配备8元件平面接收阵列。一旦所有目标被检测到，绑定限制源于竞争而非灵敏度——约74%的相干处理间隔中，两个目标共享一个最近检测结果，且目标被检测到的频率远高于被跟踪的频率。该阵列的仰角无法区分共享距离-多普勒单元的目标，但在58%的间隔中决定了关联关系。并行跟踪由RAN智能控制器（RIC）xApp通过SAPIENT接口导出至指挥控制融合节点，该接口传输经校准的检测置信度，并在模式级别针对模拟融合节点进行验证。本次评估仅在单一几何结构下使用10个噪声种子进行仿真，对相关机制进行了表征与分析，以确定影响多目标跟踪性能的关键因素。
英文摘要
We evaluate multi-target detection, association and tracking end to end on an O-RAN simulation testbed built from OpenAirInterface, FlexRIC and Sionna RT that repurposes the 5G NR uplink sounding reference signal as a passive radar waveform, and against what a counter-UAS command-and- control (C2) consumer requires rather than by detection alone. Three UAVs differing in altitude, velocity and radar cross section (-8 to -20 dBsm) fly one bistatic pair with an 8-element planar receive array. Once every target is detected the binding limit is contention, not sensitivity - two targets share one nearest detection in about 74% of coherent processing intervals, and targets are detected far more often than they are tracked. Elevation from that array cannot separate targets sharing a range-Doppler cell, but it decides association in 58% of intervals. Concurrent tracks are exported from the RAN Intelligent Controller (RIC) xApp to a C2 fusion node over a SAPIENT interface carrying a calibrated detection confidence, validated at schema level against a mock fusion node. The evaluation is emulation-only on one geometry with 10 noise seeds, the mechanisms are characterized and analyzed to identify the key factors influencing multi-target tracking performance.
Comments25 pages, 3 figures, 12 tables
